At the core of big data applications and services are machine learning models that analyze large volumes of data to deliver various insights, key performance indicators, and other actionable information to the users of the applications and services. Designers may differentiate machine learning models, or machine learning algorithms (MLAs) for different big data applications involving video, speech, text, location information, images, network traffic data, and so forth. For example, different machine learning models (derived from corresponding MLAs) may include support vector machine (SVMs), e.g., binary classifiers and/or linear binary classifiers, multi-class classifiers, kernel-based SVMs, or the like, a distance-based classifier, a decision tree algorithm/model, a k-nearest neighbor (KNN) algorithm/model, and so on.